Systems and methods for filtering page recommendations

ABSTRACT

Systems, methods, and non-transitory computer-readable media can determine a profile model for a page that is accessible through the social networking system, the profile model describing one or more modal characteristics of users of the social networking system that have fanned the page. A determination can be made that the page should be recommended to a first user of the social networking system based at least in part on the profile model. At least one page recommendation that references the page can be provided to the first user.

FIELD OF THE INVENTION

The present technology relates to the field of content provision. Moreparticularly, the present technology relates to techniques for filteringpage recommendations.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices to, forexample, interact with one another, access content, share content, andcreate content. In some cases, content items can include postings frommembers of a social network. The postings may include text and mediacontent items, such as images, videos, and audio. The postings may bepublished to the social network for consumption by others.

Under conventional approaches, a user may navigate to or be presentedwith various content items in a social network. The content items cancome from pages associated with members of the social network. In someinstances, the content items may be of high interest to the user. If theuser expresses interest in a particular content item, the social networkmay attempt, based on the content item, to provide to the useradditional content items that would also be of interest to the user.Providing such additional content items can enhance the user experienceand may help realize the full potential of the social network.Unfortunately, attempts to provide such additional content items and tomaintain a high level of interest from the user often fail. The growingsize of social networks can also pose problems with respect to the goalof providing content items of high interest to the user. As availablecontent grows in amount, in theory, the likelihood of finding morecontent items of high interest to the user should increase. However, inpractice, the ability to identify content items of high interest to theuser can be complicated by the sheer volume of content.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured todetermine a profile model for a page that is accessible through thesocial networking system, the profile model describing one or more modalcharacteristics of users of the social networking system that havefanned the page. A determination can be made that the page should berecommended to a first user of the social networking system based atleast in part on the profile model. At least one page recommendationthat references the page can be provided to the first user.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to determine at least one probabilitydistribution for a profile setting, the probability distribution beingconstructed using values provided for the profile setting by the usersthat have fanned the page and determine the one or more modalcharacteristics corresponding to the page based at least in part on theprobability distribution.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to cluster the values into one or morebins based at least in part on a semantic similarity or a stringsimilarity.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to provide page recommendations thatreference the page to a plurality of users of the social networkingsystem that specified a profile setting value that corresponds to afirst modal characteristic in the one or more modal characteristics,determine that a threshold percentage of the page recommendationsresulted in at least one conversion by the users, and associate thefirst modal characteristic with the page.

In an embodiment, the profile setting corresponds to one of thefollowing characteristics: age, gender, gender preference, relationshipstatus, occupation, workplace, education level, an institution of whichthe user is an alumni, religious affiliation, political affiliation,marital status, parental status, or causes supported by the user.

In an embodiment, at least some of the values provided for the profilesetting by the users that have fanned the page are weighted, wherein theweighting of a value specified by a user is based at least in part on arespective affinity between the user and the page.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to determine that the first user of thesocial networking system has specified at least one profile settingvalue that corresponds to at least one of the modal characteristics.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to determine that the page should not berecommended to a second user of the social networking system based atleast in part on the profile model and filter the page from beingrecommended to the second user.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to determine that no profile settingvalues specified by the second user correspond to the one or more modalcharacteristics.

In an embodiment, the systems, methods, and non-transitory computerreadable media are configured to generate a trained machine learningmodel for the page, the machine learning model being trained to predictwhether profile setting values specified by a user correspond to the oneor more modal characteristics of users that have fanned the page.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example page module,according to an embodiment of the present disclosure.

FIG. 2 illustrates an example of a page profile modeling moduleconfigured to determine respective profile models for pages, accordingto an embodiment of the present disclosure.

FIG. 3 illustrates an example of a page recommendation module configuredto provide page recommendations, according to an embodiment of thepresent disclosure.

FIG. 4 illustrates an example process for determining pagerecommendations, according to an embodiment of the present disclosure.

FIG. 5 illustrates example process for determining a profile model for apage, according to an embodiment of the present disclosure.

FIG. 6 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 7 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION Approaches for Filtering Page Recommendations

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices to, forexample, interact with one another, access content, share content, andcreate content. In some cases, content items can include postings frommembers of a social network. The postings may include text and mediacontent items, such as images, videos, and audio. The postings may bepublished to the social network for consumption by others.

Under conventional approaches, a user may navigate to or be presentedwith various content items in a social network. The content items cancome from pages associated with members of the social network. In someinstances, the content items may be of high interest to the user. If theuser expresses interest in a particular content item, the social networkmay attempt, based on the content item, to provide to the useradditional content items that would also be of interest to the user.Providing such additional content items can enhance the user experienceand may help realize the full potential of the social network.Unfortunately, attempts to provide such additional content items and tomaintain a high level of interest from the user often fail. The growingsize of social networks can also pose problems with respect to the goalof providing content items of high interest to the user. As availablecontent grows in amount, in theory, the likelihood of finding morecontent items of high interest to the user should increase. However, inpractice, the ability to identify content items of high interest to theuser can be complicated by the sheer volume of content. Accordingly,such conventional approaches may not be effective in addressing theseand other problems arising in computer technology.

An improved approach rooted in computer technology overcomes theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology. Invarious embodiments, profiles created by users of a social networkingsystem can be used to determine various characteristics (e.g., profilesetting values) that are generally shared among users that areassociated with a page. In some embodiments, characteristics that aremodal (e.g., characteristics shared among a threshold portion of theusers) can be used to recommend pages to other users that share the samecharacteristics. For example, a threshold portion of users associatedwith a page for “Amateur Radio Enthusiasts” may have specified“engineer” as their occupation in their respective profiles. Based onthis observation, the page “Amateur Radio Enthusiasts” can berecommended to users that are not associated with the page but that havealso specified “engineer” as their occupation. In another example,recommendation of the page “Amateur Radio Enthusiasts” may be filtered,or not recommended, for users that have specified their occupation asbeing something other than “engineer”.

In general, pages can be accessed through a social networking system. Apage may correspond to an entity (e.g., a business, topic, location,user, etc.). Users of the social networking system can navigate to thepage to learn more about the entity as well as access and/or postcontent through the page. Such content may include text and/or mediacontent items, such as images, videos, and audio. Users of the socialnetworking system have the option to be associated with the page, forexample, by “liking” the page (e.g., selecting a “like” option throughthe social networking system) or by becoming a “fan” the page (e.g.,selecting a “fanning” option through the social networking system). Auser that is associated with a page can be referred to as a “fan” orsomeone who has “fanned” the page. In some instances, the socialnetworking system can include content items that are posted to the pagein the respective news feeds of users that are associated with the page.

Users of the social networking system can each have a corresponding userprofile. Through their respective profiles, users can share variousinformation about themselves, such as their characteristics. Forexample, a user can specify values for profile settings that correspondto characteristics such as age, gender, gender preference, relationshipstatus, occupation and/or workplace, education level, one or moreinstitutions of which the user is an alumni, religious affiliation, andpolitical affiliation. These profile settings are provided merely asexamples and, naturally, users may be able to set additional profilesettings to describe other characteristics, such as, for example,marital status, whether the user is a parent, whether the user supportsa particular cause, to name some examples. A value for a profile settingcan be specified, for example, by providing input in a freeform field orby selecting an option from a set of options associated with the profilesetting. Profile settings specified by the user can be published in theuser's profile through the social networking system. Depending on theuser's privacy settings, some, or all, of the profile settings may bemade available publically (e.g., accessible by all users of the socialnetworking system) or privately (e.g., accessible by a limited set ofusers of the social networking system).

FIG. 1 illustrates an example system 100 including an example pagemodule 102, according to an embodiment of the present disclosure. Asshown in the example of FIG. 1, the page module 102 can include a pageprofile modeling module 104 and a page recommendation module 106. Insome instances, the example system 100 can include at least one datastore 108. The components (e.g., modules, elements, etc.) shown in thisfigure and all figures herein are exemplary only, and otherimplementations may include additional, fewer, integrated, or differentcomponents. Some components may not be shown so as not to obscurerelevant details.

In some embodiments, the page module 102 can be implemented, in part orin whole, as software, hardware, or any combination thereof. In general,a module as discussed herein can be associated with software, hardware,or any combination thereof. In some implementations, one or morefunctions, tasks, and/or operations of modules can be carried out orperformed by software routines, software processes, hardware, and/or anycombination thereof. In some cases, the page module 102 can beimplemented, in part or in whole, as software running on one or morecomputing devices or systems, such as on a user or client computingdevice. In one example, the page module 102 or at least a portionthereof can be implemented as or within an application (e.g., app), aprogram, or an applet, etc., running on a user computing device or aclient computing system, such as the user device 610 of FIG. 6. Inanother example, the page module 102 or at least a portion thereof canbe implemented using one or more computing devices or systems thatinclude one or more servers, such as network servers or cloud servers.In some instances, the page module 102 can, in part or in whole, beimplemented within or configured to operate in conjunction with a socialnetworking system (or service), such as the social networking system 630of FIG. 6.

The page module 102 can be configured to communicate and/or operate withthe at least one data store 108, as shown in the example system 100. Theat least one data store 108 can be configured to store and maintainvarious types of data including audience data that identifies users ofthe social networking system that have fanned pages that are availablefor access through the social networking system. The audience data canalso describe corresponding values for profile settings specified byusers of the social networking system. In some implementations, the atleast one data store 108 can store information associated with thesocial networking system (e.g., the social networking system 630 of FIG.6). The information associated with the social networking system caninclude data about users, social connections, social interactions,locations, geo-fenced areas, maps, places, events, pages, groups, posts,communications, content, feeds, account settings, privacy settings, asocial graph, and various other types of data. In some implementations,the at least one data store 108 can store information associated withusers, such as user identifiers, user information, profile information,user specified settings, content produced or posted by users, andvarious other types of user data.

In various embodiments, the page profile modeling module 104 can beconfigured to generate respective profile models for pages accessiblethrough the social networking system. More details regarding the pageprofile modeling module 104 will be provided below with reference toFIG. 2. The page recommendation module 106 can be configured to utilizeprofile models that correspond to pages for purposes of recommending, ornot recommending, pages to users of the social networking system. Moredetails regarding the page recommendation module 106 will be providedbelow with reference to FIG. 3.

FIG. 2 illustrates an example of a page profile modeling module 202configured to determine respective profile models for pages, accordingto an embodiment of the present disclosure. In some embodiments, thepage profile modeling module 104 of FIG. 1 can be implemented as thepage profile modeling module 202. As shown in FIG. 2, the page profilemodeling module 202 can include a fan sampling module 204 and a modelgenerating module 206.

As mentioned, in various embodiments, profiles created by users of asocial networking system can be used to determine variouscharacteristics (e.g., profile setting values) that are generally, orpredominantly, shared among users that are associated with a page. Forexample, in some embodiments, characteristics that are modal (e.g.,characteristics shared among a threshold portion of the users) can beused to recommend pages to other users that share the modalcharacteristics or to filter page recommendations for other users thatdo not share the modal characteristics.

When determining a profile model for a page, in some embodiments, thefan sampling module 204 can be configured to determine a set of usersassociated with the page whose profile setting values will be analyzedfor purposes of constructing the profile model. In some embodiments, theprofiles of all fans of the page are used to determine the profilemodel. Alternatively, in some embodiments, the profiles of a portion, orsubset, of the fans of the page are used to determine the profile model.By selecting a portion of users, the fan sampling module 204 can reducethe number of user profiles that are needed for purposes of determiningthe profile model. For example, the fan sampling module 204 can sample asubset of users with a maximum size x. The size of x is tunabledepending on the amount of computing resources that are available forcomputing the profile model. In some embodiments, the fans of a page canbe sampled depending on whether their interactions with the page arewithin some threshold period of time. For example, the sampling may bedone using fans of the page that are active on the social networkingsystem on a daily basis, monthly basis, or some other arbitrary periodof time. In some embodiments, the sampling may be done using fans of thepage that interact with the page on a daily basis, monthly basis, orsome other arbitrary period of time.

In some embodiments, the fan sampling module 204 can assign respectiveweights to the user profiles being used to determine the profile model,so that the profiles of users that are more engaged with the page areweighted more than the profiles of users that are less engaged. In oneexample, the relationship strength, or affinity, between a user and thepage can be measured based on how recently the user fanned the page. Ifthe user fanned the page recently, then the fan sampling module 204 canassign a higher weight to the user's profile. In contrast, if the userfanned the page over a year ago, then the fan sampling module 204 canassign a lower weight to the user's profile. In one example, therelationship strength between a user and the page can be measured basedon the user's interactions with the page as well as the rate, orfrequency, at which such interactions were performed. The user mayinteract with the page in a number of ways including, for example,accessing (e.g., visiting) the page, accessing (e.g., reading, clicking,etc.) a content item that was posted in the page, liking a content itemthat was posted in the page, adding a comment to a content item that wasposted in the page, and sending a message to an administrator of thepage. Some interactions can more representative of the user'srelationship with the page than others and, therefore, each interactionmay be weighted differently. For example, adding comments to contentitems posted in the page and messaging administrators of the page caneach be weighted higher than accessing the page or accessing a contentitem that was posted in the page. If the user has not interacted withthe page after fanning the page, then the fan sampling module 204 canassign an even lower weight. The actual weights used, and any adjustmentto such weights, can vary depending on the implementation. However, anyrubric may be used for weighting of user profiles for purposes ofgenerating profile models.

The model generating module 206 can be configured to generate theprofile model for the page using all, or a subset, of the profiles ofusers associated with the page. Depending on the implementation, theseprofiles may or may not be weighted, as described above. In someembodiments, the model generating module 206 can be configured togenerate the profile model for the page by constructing respectiveprobability distributions, or histograms, for some, or all, of theprofile settings specified by fans of the page. Thus, in someembodiments, a profile model for the page may reference severalprobability distributions that each describe the distribution of thepage's fans in terms of a particular characteristic (i.e., profilesetting). For example, a page may have a first probability distributionfor age, a second probability distribution for gender, and a thirdprobability distribution for occupation. A probability distribution fora characteristic (e.g., profile setting) can include respective binsthat each correspond to the values that were provided by fans of thepage for that profile setting. For example, a probability distributionfor occupation can include the bins “professor”, “marketing analyst”,“researcher”, “software engineer”, “student”, and “null” (for users thathave not specified a profile setting value for occupation). Each fan ofthe page can be assigned to one of the bins based on the occupationdefined in their profile setting. Once the fans have been assigned tothe bins, the model generating module 206 can determine whether theprobability distribution has a threshold modal distribution. Forexample, in some embodiments, the occupation “software engineer” may bedetermined to be modal because that occupation was listed mostfrequently as an occupation by fans of the page. In some embodiments,when determining whether a profile setting value is modal, the modelgenerating module 206 can determine whether a threshold proportion ofthe page's fans (e.g., some percentage of the fans) have listed thatvalue for their profile setting. This threshold may vary depending onthe page. For example, a first page may require a profile setting valueto be specified by 40 percent of the page's fans before qualifying as amodality while a second page may require a profile setting value to bespecified by 80 percent of the page's fans before qualifying as amodality. In some instances, the probability distribution for a profilesetting may be multi-modal. For example, based on the probabilitydistribution, the model generating module 206 may determine that both“software engineer” and “student” were listed as occupations bythreshold proportion of the page's fans. In this example, both theoccupations “software engineer” and “student” may both be determined tobe modal.

In some embodiments, the model generating module 206 can evaluate anymodalities determined for the page for accuracy before using themodalities for recommending the page or for filtering recommendationsfor the page. For example, the occupation “software engineer” may bedetermined to be modal for a page. In this example, the page may berecommended to users of the social networking system that have “softwareengineer” listed as their occupation. In such embodiments, the modelgenerating module 206 can determine whether a threshold number, orpercentage, of conversions result from users that were recommended thepage based on their respective occupations being listed as “softwareengineer”. A conversion may include, for example, fanning the page,accessing (e.g., visiting) the page, accessing (e.g., reading, clicking,etc.) a content item that was posted in the page, liking a content itemthat was posted in the page, adding a comment to a content item that wasposted in the page, and sending a message to an administrator of thepage. Modalities that demonstrate a threshold amount of correlation withthe page can be stored as a profile model for the page. This profilemodel can then be used for recommending the page or for filteringrecommendations for the page based on whether a user shares some, orall, of the modal characteristics (i.e., profile setting values) for thepage.

In some embodiments, the model generating module 206 can be configuredto cluster values provided by users for various profile settings basedon their similarity. For example, the occupations listed by a page'sfans may include “mechanical engineer”, “electrical engineer”, “softwareengineer”, “chemical engineer”, “social worker”, and “financialanalyst”. In some instances, computing a probability distribution thatincludes separate bins that each correspond one of these occupations maynot be optimal. In such instances, the model generating module 206 canbe configured to cluster occupations based on their similarity. In theexample above, the occupations “mechanical engineer”, “electricalengineer”, “software engineer”, and “chemical engineer” can be clusteredinto one bin associated with the occupation “engineer”. Such clusteringof profile setting values may be performed using any generally knownapproach for clustering strings. Depending on the implementation, thesimilarity between two profile setting values may be measured based onsemantic similarity and/or string similarity (e.g., edit distance). Insome embodiments, some profile settings can be bucketed to facilitatethe distribution analysis. For example, users can be assigned to binsthat correspond to age ranges (e.g., ages 12-17, 18-24, 25-34, 35-44,45-54, 55-64, 64+) based on their age profile setting.

In some embodiments, the model generating module 206 can be configuredto generate the profile model for the page by training a machinelearning (ML) model that corresponds to the page. The trained ML modelfor the page can predict, using profile settings for a user, alikelihood that measures whether the user shares one or more profilesetting values that are modal for the page. In some embodiments, the MLmodel can be trained using a set of positive examples that includeprofile settings of some, or all, of the page's fans, which may or maynot be weighted, as described above. The ML model can also be trainedusing a set of negative examples that include profile settings ofrandomly selected users of the social networking system. As describedabove, the model generating module 206 can evaluate any predictions madeusing the trained ML model for accuracy and for re-training the ML modelbased on the accuracy or inaccuracy of the prediction.

FIG. 3 illustrates an example of a page recommendation module 302configured to provide page recommendations, according to an embodimentof the present disclosure. In some embodiments, the page recommendationmodule 106 of FIG. 1 can be implemented as the page recommendationmodule 302. As shown in FIG. 3, the page recommendation module 302 caninclude a user profile module 304 and a recommendation filtering module306.

The user profile module 304 can be configured to obtain a profileassociated with a candidate user to whom a page recommendation may bemade. As mentioned, through this profile, the user can specify valuesfor profile settings that correspond to various characteristics, such asage, gender, gender preference, relationship status, occupation and/orworkplace, education level, one or more institutions of which the useris an alumni, religious affiliation, and political affiliation. Theseprofile settings are provided merely as examples and, naturally, usersmay be able to set additional profile settings to describe othercharacteristics.

The recommendation filtering module 306 can be configured to determinewhether a page should, or should not, be recommended to the candidateuser, for example, based on the profile setting values specified in theuser's profile and a profile model for the page. In some embodiments,the recommendation filtering module 306 determines whether any of theuser's profile setting values correspond to one or more modalitiesreferenced in the profile model that was determined for the page. Insuch embodiments, the recommendation filtering module 306 can recommendthe page to the user if any of the user's profile setting valuescorrespond to the one or more modalities for the page. In someembodiments, the recommendation filtering module 306 can filter, orprevent, the page from recommended to the user if none of the user'sprofile setting values correspond to the one or more modalities for thepage. In some embodiments, the recommendation filtering module 306 canbe configured to determine whether the page should, or should not, berecommended to the user based on a trained ML model, as described above.

FIG. 4 illustrates an example process 400 for determining pagerecommendations, according to an embodiment of the present disclosure.It should be appreciated that there can be additional, fewer, oralternative steps performed in similar or alternative orders, or inparallel, within the scope of the various embodiments discussed hereinunless otherwise stated. At block 402, a profile model for a page thatis accessible through the social networking system is determined. Theprofile model can describe one or more modal characteristics of users ofthe social networking system that have fanned the page. At block 404, adetermination can be made that the page should be recommended to a firstuser of the social networking system based at least in part on theprofile model. At block 406, at least one page recommendation thatreferences the page can be provided to the first user.

FIG. 5 illustrates example process 500 for determining a profile modelfor a page, according to an embodiment of the present disclosure. Itshould be appreciated that there can be additional, fewer, oralternative steps performed in similar or alternative orders, or inparallel, within the scope of the various embodiments discussed hereinunless otherwise stated. At block 502, a set of profile setting valuesare obtained for users that have fanned a page. At block 504, the valuesare clustered into one or more bins based at least in part on a semanticsimilarity or a string similarity. At block 506, a probabilitydistribution for the profile setting is constructed using the valuesthat were provided by the users that have fanned the page. At block 508,one or more modal characteristics corresponding to the page aredetermined based at least in part on the probability distribution.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 6 illustrates a network diagram of an example system 600 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 600 includes one or more user devices610, one or more external systems 620, a social networking system (orservice) 630, and a network 650. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 630. For purposes of illustration, the embodiment of the system600, shown by FIG. 6, includes a single external system 620 and a singleuser device 610. However, in other embodiments, the system 600 mayinclude more user devices 610 and/or more external systems 620. Incertain embodiments, the social networking system 630 is operated by asocial network provider, whereas the external systems 620 are separatefrom the social networking system 630 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 630 and the external systems 620 operate inconjunction to provide social networking services to users (or members)of the social networking system 630. In this sense, the socialnetworking system 630 provides a platform or backbone, which othersystems, such as external systems 620, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 610 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 650. In one embodiment, the user device 610 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 610 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 610 is configured tocommunicate via the network 650. The user device 610 can execute anapplication, for example, a browser application that allows a user ofthe user device 610 to interact with the social networking system 630.In another embodiment, the user device 610 interacts with the socialnetworking system 630 through an application programming interface (API)provided by the native operating system of the user device 610, such asiOS and ANDROID. The user device 610 is configured to communicate withthe external system 620 and the social networking system 630 via thenetwork 650, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 650 uses standard communicationstechnologies and protocols. Thus, the network 650 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network650 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 650 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 610 may display content from theexternal system 620 and/or from the social networking system 630 byprocessing a markup language document 614 received from the externalsystem 620 and from the social networking system 630 using a browserapplication 612. The markup language document 614 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 614, the browser application 612 displays the identifiedcontent using the format or presentation described by the markuplanguage document 614. For example, the markup language document 614includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 620 and the social networking system 630. In variousembodiments, the markup language document 614 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 614 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 620 andthe user device 610. The browser application 612 on the user device 610may use a JavaScript compiler to decode the markup language document614.

The markup language document 614 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 610 also includes one or more cookies616 including data indicating whether a user of the user device 610 islogged into the social networking system 630, which may enablemodification of the data communicated from the social networking system630 to the user device 610.

The external system 620 includes one or more web servers that includeone or more web pages 622 a, 622 b, which are communicated to the userdevice 610 using the network 650. The external system 620 is separatefrom the social networking system 630. For example, the external system620 is associated with a first domain, while the social networkingsystem 630 is associated with a separate social networking domain. Webpages 622 a, 622 b, included in the external system 620, comprise markuplanguage documents 614 identifying content and including instructionsspecifying formatting or presentation of the identified content. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities.

The social networking system 630 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 630 may be administered, managed, or controlled by anoperator. The operator of the social networking system 630 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 630. Any type of operator may beused.

Users may join the social networking system 630 and then add connectionsto any number of other users of the social networking system 630 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 630 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 630. For example, in an embodiment, if users in thesocial networking system 630 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 630 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 630 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 630 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 630 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system630 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 630 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system630 provides users with the ability to take actions on various types ofitems supported by the social networking system 630. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 630 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 630, transactions that allow users to buy or sellitems via services provided by or through the social networking system630, and interactions with advertisements that a user may perform on oroff the social networking system 630. These are just a few examples ofthe items upon which a user may act on the social networking system 630,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 630 or inthe external system 620, separate from the social networking system 630,or coupled to the social networking system 630 via the network 650.

The social networking system 630 is also capable of linking a variety ofentities. For example, the social networking system 630 enables users tointeract with each other as well as external systems 620 or otherentities through an API, a web service, or other communication channels.The social networking system 630 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 630. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 630 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 630 also includes user-generated content,which enhances a user's interactions with the social networking system630. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 630. For example, a usercommunicates posts to the social networking system 630 from a userdevice 610. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 630 by a third party. Content“items” are represented as objects in the social networking system 630.In this way, users of the social networking system 630 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 630.

The social networking system 630 includes a web server 632, an APIrequest server 634, a user profile store 636, a connection store 638, anaction logger 640, an activity log 642, and an authorization server 644.In an embodiment of the invention, the social networking system 630 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 636 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 630. This information is storedin the user profile store 636 such that each user is uniquelyidentified. The social networking system 630 also stores data describingone or more connections between different users in the connection store638. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 630 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 630, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 638.

The social networking system 630 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 636and the connection store 638 store instances of the corresponding typeof objects maintained by the social networking system 630. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store636 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 630initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 630, the social networking system 630 generatesa new instance of a user profile in the user profile store 636, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 638 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 620 or connections to other entities. The connection store 638may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 636 and the connection store 638 may beimplemented as a federated database.

Data stored in the connection store 638, the user profile store 636, andthe activity log 642 enables the social networking system 630 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 630, user accounts of thefirst user and the second user from the user profile store 636 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 638 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 630. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 630 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 630). The image may itself be represented as a node in the socialnetworking system 630. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 636, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 642. By generating and maintaining thesocial graph, the social networking system 630 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 632 links the social networking system 630 to one or moreuser devices 610 and/or one or more external systems 620 via the network650. The web server 632 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 632 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system630 and one or more user devices 610. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 634 allows one or more external systems 620 anduser devices 610 to call access information from the social networkingsystem 630 by calling one or more API functions. The API request server634 may also allow external systems 620 to send information to thesocial networking system 630 by calling APIs. The external system 620,in one embodiment, sends an API request to the social networking system630 via the network 650, and the API request server 634 receives the APIrequest. The API request server 634 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 634 communicates to the external system 620via the network 650. For example, responsive to an API request, the APIrequest server 634 collects data associated with a user, such as theuser's connections that have logged into the external system 620, andcommunicates the collected data to the external system 620. In anotherembodiment, the user device 610 communicates with the social networkingsystem 630 via APIs in the same manner as external systems 620.

The action logger 640 is capable of receiving communications from theweb server 632 about user actions on and/or off the social networkingsystem 630. The action logger 640 populates the activity log 642 withinformation about user actions, enabling the social networking system630 to discover various actions taken by its users within the socialnetworking system 630 and outside of the social networking system 630.Any action that a particular user takes with respect to another node onthe social networking system 630 may be associated with each user'saccount, through information maintained in the activity log 642 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 630 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 630, the action isrecorded in the activity log 642. In one embodiment, the socialnetworking system 630 maintains the activity log 642 as a database ofentries. When an action is taken within the social networking system630, an entry for the action is added to the activity log 642. Theactivity log 642 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 630,such as an external system 620 that is separate from the socialnetworking system 630. For example, the action logger 640 may receivedata describing a user's interaction with an external system 620 fromthe web server 632. In this example, the external system 620 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system620 include a user expressing an interest in an external system 620 oranother entity, a user posting a comment to the social networking system630 that discusses an external system 620 or a web page 622 a within theexternal system 620, a user posting to the social networking system 630a Uniform Resource Locator (URL) or other identifier associated with anexternal system 620, a user attending an event associated with anexternal system 620, or any other action by a user that is related to anexternal system 620. Thus, the activity log 642 may include actionsdescribing interactions between a user of the social networking system630 and an external system 620 that is separate from the socialnetworking system 630.

The authorization server 644 enforces one or more privacy settings ofthe users of the social networking system 630. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 620, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems620. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 620 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 620 toaccess the user's work information, but specify a list of externalsystems 620 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 620 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 644 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 620, and/or other applications and entities. Theexternal system 620 may need authorization from the authorization server644 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 644 determines if another user, the external system620, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 630 can include a pagemodule 646. The page module 646 can, for example, be implemented as thepage module 102 of FIG. 1. As discussed previously, it should beappreciated that there can be many variations or other possibilities.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 7 illustrates anexample of a computer system 700 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 700 includes sets ofinstructions for causing the computer system 700 to perform theprocesses and features discussed herein. The computer system 700 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 700 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 700 may be the social networking system 630, the user device 610,and the external system 720, or a component thereof. In an embodiment ofthe invention, the computer system 700 may be one server among many thatconstitutes all or part of the social networking system 630.

The computer system 700 includes a processor 702, a cache 704, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 700 includes a high performanceinput/output (I/O) bus 706 and a standard I/O bus 708. A host bridge 710couples processor 702 to high performance I/O bus 706, whereas I/O busbridge 712 couples the two buses 706 and 708 to each other. A systemmemory 714 and one or more network interfaces 716 couple to highperformance I/O bus 706. The computer system 700 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 718 and I/O ports 720 couple to the standard I/Obus 708. The computer system 700 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 708. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 700, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 700 are described in greater detailbelow. In particular, the network interface 716 provides communicationbetween the computer system 700 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 718 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 714 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor702. The I/O ports 720 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 700.

The computer system 700 may include a variety of system architectures,and various components of the computer system 700 may be rearranged. Forexample, the cache 704 may be on-chip with processor 702. Alternatively,the cache 704 and the processor 702 may be packed together as a“processor module”, with processor 702 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 708 may couple to thehigh performance I/O bus 706. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 700being coupled to the single bus. Moreover, the computer system 700 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 700 that, when read and executed by one or moreprocessors, cause the computer system 700 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system700, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 702.Initially, the series of instructions may be stored on a storage device,such as the mass storage 718. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 716. The instructions are copied from thestorage device, such as the mass storage 718, into the system memory 714and then accessed and executed by the processor 702. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system700 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:determining, by a computing system, a profile model for a page that isaccessible through the computing system, the profile model describingone or more modal characteristics of users of the computing system thathave fanned the page, the determining further comprising: determining,by the computing system, at least one probability distribution for aprofile setting, the probability distribution being constructed usingvalues associated with the profile setting by the users that have fannedthe page; determining, by the computing system, the one or more modalcharacteristics based at least in part on the probability distribution;providing, by the computing system, page recommendations that referencethe page to a plurality of users of the computing system that specifiedat least one profile setting value that corresponds to a first modalcharacteristic in the one or more modal characteristics; determining, bythe computing system, that a threshold amount of the pagerecommendations resulted in at least one conversion by the users;associating, by the computing system, the first modal characteristicwith the page; determining, by the computing system, that no profilesetting values specified by a second user satisfy the first modalcharacteristic associated with the page; and filtering, by the computingsystem, the page from being recommended to the second user.
 2. Thecomputer-implemented method of claim 1, the method further comprising:clustering, by the computing system, the values into one or more binsbased at least in part on a semantic similarity or a string similarity.3. The computer-implemented method of claim 1, wherein the profilesetting corresponds to at least one of the following characteristics:age, gender, gender preference, relationship status, occupation,workplace, education level, an institution of which the user is analumni, religious affiliation, political affiliation, marital status,parental status, or causes supported by the user.
 4. Thecomputer-implemented method of claim 1, wherein one or more of thevalues provided for the profile setting by the users that have fannedthe page are weighted, wherein the weighting of a value specified by auser is based at least in part on a respective affinity between the userand the page.
 5. The computer-implemented method of claim 1, whereindetermining the profile model for the page further comprises:generating, by the computing system, a trained machine learning modelfor the page, the machine learning model being trained to predictwhether profile setting values specified by a user correspond to the oneor more modal characteristics of users that have fanned the page.
 6. Thecomputer-implemented method of claim 1, the method further comprising:determining, by the computing system, that the page should berecommended to a first user of the computing system, the determiningfurther comprising: determining, by the computing system, that the firstuser of the computing system has specified at least one profile settingvalue that corresponds to the first modal characteristic associated withthe page; and providing, by the computing system, at least one pagerecommendation to the first user that references the page.
 7. A systemcomprising: at least one processor; and a memory storing instructionsthat, when executed by the at least one processor, cause the system toperform: determining a profile model for a page that is accessiblethrough the system, the profile model describing one or more modalcharacteristics of users of the system that have fanned the page, thedetermining further comprising: determining at least one probabilitydistribution for a profile setting, the probability distribution beingconstructed using values associated with the profile setting by theusers that have fanned the page; determining the one or more modalcharacteristics based at least in part on the probability distribution;providing page recommendations that reference the page to a plurality ofusers of the system that specified at least one profile setting valuethat corresponds to a first modal characteristic in the one or moremodal characteristics; determining that a threshold amount of the pagerecommendations resulted in at least one conversion by the users;associating the first modal characteristic with the page; determiningthat no profile setting values specified by a second user satisfy thefirst modal characteristic associated with the page; and filtering thepage from being recommended to the second user.
 8. The system of claim7, wherein the system further performs: clustering the values into oneor more bins based at least in part on a semantic similarity or a stringsimilarity.
 9. The system of claim 7, wherein the profile settingcorresponds to one of the following characteristics: age, gender, genderpreference, relationship status, occupation, workplace, education level,an institution of which the user is an alumni, religious affiliation,political affiliation, marital status, parental status, or causessupported by the user.
 10. The system of claim 7, wherein the systemfurther performs: determining that the page should be recommended to afirst user of the system, the determining further comprising:determining that the first user of the system has specified at least oneprofile setting value that corresponds to the first modal characteristicassociated with the page; and providing at least one page recommendationto the first user that references the page.
 11. The system of claim 7,wherein the profile setting corresponds to at least one of the followingcharacteristics: age, gender, gender preference, relationship status,occupation, workplace, education level, an institution of which the useris an alumni, religious affiliation, political affiliation, maritalstatus, parental status, or causes supported by the user.
 12. The systemof claim 7, wherein one or more of the values provided for the profilesetting by the users that have fanned the page are weighted, wherein theweighting of a value specified by a user is based at least in part on arespective affinity between the user and the page.
 13. The system ofclaim 7, wherein the system further performs: generating a trainedmachine learning model for the page, the machine learning model beingtrained to predict whether profile setting values specified by a usercorrespond to the one or more modal characteristics of users that havefanned the page.
 14. A non-transitory computer-readable storage mediumincluding instructions that, when executed by at least one processor ofa computing system, cause the computing system to perform a methodcomprising: determining a profile model for a page that is accessiblethrough the computing system, the profile model describing one or moremodal characteristics of users of the computing system that have fannedthe page, the determining further comprising: determining at least oneprobability distribution for a profile setting, the probabilitydistribution being constructed using values associated with the profilesetting by the users that have fanned the page; determining the one ormore modal characteristics based at least in part on the probabilitydistribution; providing page recommendations that reference the page toa plurality of users of the computing system that specified at least oneprofile setting value that corresponds to a first modal characteristicin the one or more modal characteristics; determining that a thresholdamount of the page recommendations resulted in at least one conversionby the users; associating the first modal characteristic with the page;determining that no profile setting values specified by a second usersatisfy the first modal characteristic associated with the page; andfiltering the page from being recommended to the second user.
 15. Thenon-transitory computer-readable storage medium of claim 14, wherein thecomputing system further performs: clustering the values into one ormore bins based at least in part on a semantic similarity or a stringsimilarity.
 16. The non-transitory computer-readable storage medium ofclaim 14, wherein the profile setting corresponds to one of thefollowing characteristics: age, gender, gender preference, relationshipstatus, occupation, workplace, education level, an institution of whichthe user is an alumni, religious affiliation, political affiliation,marital status, parental status, or causes supported by the user. 17.The non-transitory computer-readable storage medium of claim 14, whereinthe computing system further performs: determining that the page shouldbe recommended to a first user of the computing system, the determiningfurther comprising: determining that the first user of the computingsystem has specified at least one profile setting value that correspondsto the first modal characteristic associated with the page; andproviding at least one page recommendation to the first user thatreferences the page.
 18. The non-transitory computer-readable storagemedium of claim 14, wherein the profile setting corresponds to at leastone of the following characteristics: age, gender, gender preference,relationship status, occupation, workplace, education level, aninstitution of which the user is an alumni, religious affiliation,political affiliation, marital status, parental status, or causessupported by the user.
 19. The non-transitory computer-readable storagemedium of claim 14, wherein one or more of the values provided for theprofile setting by the users that have fanned the page are weighted,wherein the weighting of a value specified by a user is based at leastin part on a respective affinity between the user and the page.
 20. Thenon-transitory computer-readable storage medium of claim 14, wherein thecomputing system further performs: generating a trained machine learningmodel for the page, the machine learning model being trained to predictwhether profile setting values specified by a user correspond to the oneor more modal characteristics of users that have fanned the page.